Data-driven regionalization of housing markets
This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2013
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| In: |
Annals of the Association of American Geographers
Year: 2012, Volume: 103, Issue: 4, Pages: 871-889 |
| ISSN: | 1467-8306 |
| DOI: | 10.1080/00045608.2012.707587 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1080/00045608.2012.707587
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| Author Notes: | Marco Helbich, Wolfgang Brunauer, Julian Hagenauer, Michael Leitner |
| Summary: | This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The out-of-sample prediction error of a hedonic pricing model is applied to determine a “near-optimal” number of spatially coherent and homogeneous submarkets. The usefulness of this method is demonstrated with a detailed data set for the Austrian housing market. The results provide evidence that submarkets must always be considered, however they are defined, and that the proposed submarket taxonomy on a regional level significantly improves predictive quality compared to (1) a traditional pooled model, (2) a model that uses an ad hoc submarket definition based on administrative units, and (3) a model incorporating an alternative submarket definition on the basis of aspatial k-means clustering. Moreover, it is concluded that the Austrian housing market is characterized by regional determinants and that geography is the most important component determining the house prices. |
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| Item Description: | Gesehen am 31.03.2021 Published online: 04 Sep 2012 |
| Physical Description: | Online Resource |
| ISSN: | 1467-8306 |
| DOI: | 10.1080/00045608.2012.707587 |